CN113283709A - Hierarchical distributed load cooperative scheduling method for rural power grid - Google Patents

Hierarchical distributed load cooperative scheduling method for rural power grid Download PDF

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CN113283709A
CN113283709A CN202110476202.3A CN202110476202A CN113283709A CN 113283709 A CN113283709 A CN 113283709A CN 202110476202 A CN202110476202 A CN 202110476202A CN 113283709 A CN113283709 A CN 113283709A
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李勤超
卢峰
刘一民
龚超
万东
余畅
隋志远
王骁
吴成立
李�杰
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State Grid Zhejiang Electric Power Co Ltd Anji County Power Supply Co
Zhejiang Huayun Information Technology Co Ltd
Huzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Abstract

The invention provides a hierarchical distributed load cooperative scheduling method for a rural power grid, which comprises the following steps of 1: constructing a hierarchical distributed load cooperative scheduling system facing a rural power grid, and designing a diversity load scheduling flow under an emergency scene; step 2: constructing a typical industry load optimal response model in a rural power grid considering user satisfaction; and step 3: and constructing a layered distributed diversity load cooperative scheduling optimization model, and optimizing and solving a load scheduling strategy in an emergency scene. The dispatching method is obtained through modeling analysis and can solve the technical problem that the requirements of power grid side dispatching on response quantity and response speed are difficult to meet in an emergency scene in the existing rural power grid dispatching method.

Description

Hierarchical distributed load cooperative scheduling method for rural power grid
Technical Field
The invention belongs to the technical field of power supply and distribution, and particularly relates to a rural power grid-oriented hierarchical distributed load cooperative scheduling method.
Background
Along with the promotion of new rural power grid transformation and upgrading engineering, characteristic energy consumption projects and novel electric equipment are increased, rural load diversity is improved, and higher requirements are provided for power grid operation. The power demand response refers to the response under certain power system operation scenarios, such as: when an emergency seriously threatens the reliability of a power system or the system load is too high in a certain specific period of time, a power grid company sends out a price signal or an excitation signal, and a user makes a behavior of adjusting the power utilization mode. And (4) considering the requirement of user satisfaction, and researching the rural diversity load optimal response strategy. Aiming at the dispatchable load characteristics of the rural power grid, a demand response price and an incentive mechanism are designed, the power consumer is guided to adjust the power consumption behavior, the power consumption can be reduced on the basis of meeting the requirement of the user satisfaction degree, and the utility maximization of the power consumer is realized.
The new rural power distribution network has the characteristics of meshing, ecology, intellectualization and modularization, and rural loads cover a plurality of power utilization scenes such as industrial and agricultural production, tourism industry and resident life. The rural power grid has low and dispersed load density. The percentage of related users in the field of 'three farmers' in rural power grid (rural industry, agricultural production and rural life) is about 50%, the total power consumption is 12%, and the annual power consumption of a single user is 2600 kilowatt hours which is far lower than the annual power consumption of the single user in the urban power grid. The load elasticity level of a single user cannot reach the minimum level of demand response, and particularly in an emergency scene, the requirements of power grid side scheduling on the response quantity and the response speed are difficult to meet. Under the background, aiming at the current situation of a rural power grid, the characteristics of time-interval power utilization and seasonal power utilization of the rural industry are urgently needed to be met, a load aggregation business development mode is focused, a layered distributed type diversified load cooperative scheduling mode is constructed, a demand side emergency response mechanism with limited power supply capacity of the power grid is established on the premise of meeting the requirement of user satisfaction, the economy, flexibility and stability of power supply of the power grid are guaranteed, the change of rural power utilization from power utilization to power utilization is realized, and powerful guarantee is provided for the development of new rural industries.
The patent document with publication number CN109409769A discloses a rural power grid investment benefit comprehensive evaluation method based on improved set pair analysis, firstly, an investment efficiency index system and an operation effect index system of a rural power grid are established; then, constructing an improved set pair analysis 'same and different models', and solving coefficients of the models by using an improved whitening weight function; then, solving the objective weight of each index in an index system by using CRITIC; and finally, solving the contact degrees of all indexes of the evaluation object by using the improved set to the model, and performing weighted synthesis to obtain the total contact degree of the evaluation object as a final evaluation result of the evaluation object.
The invention
The method solves the problem that the investment benefit level of the rural power grid under the background of new energy grid connection and terminal energy substitution cannot be comprehensively and reasonably evaluated, constructs a hierarchical comprehensive evaluation index system of the investment benefit of the rural power grid, improves a set pair analysis model (SPA) and provides a whitening weight function solving method for the model. However, the method is more important for future planning of rural power grid construction according to the analysis result, and does not solve the technical problem that the requirements of power grid side scheduling on response quantity and response speed are difficult to meet in an emergency scene in the existing rural power grid scheduling method.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the existing rural power grid dispatching method is difficult to meet the technical problem of requirements of power grid side dispatching on response quantity and response speed in an emergency scene.
In order to solve the technical problem, the invention provides a hierarchical distributed load cooperative scheduling method for a rural power grid, which comprises the following steps:
step 1: constructing a hierarchical distributed load cooperative scheduling system facing a rural power grid, and designing a diversity load scheduling flow under an emergency scene;
step 2: constructing a typical industry load optimal response model in a rural power grid considering user satisfaction;
and step 3: and constructing a layered distributed diversity load cooperative scheduling optimization model, and optimizing and solving a load scheduling strategy in an emergency scene.
Preferably, a hierarchical distributed load cooperative scheduling system facing the rural power grid is set up in the step 1, and a specific method for designing a diversity load scheduling process in an emergency scene is as follows:
101, a hierarchical distributed diversified load cooperative scheduling system comprises a load scheduling center layer, a load aggregation business layer and a demand side resource layer; the demand side resource layer comprises direct control users, autonomous control users and small-scale users of load aggregation agent; in the hierarchical distributed diversified load cooperative scheduling system, users are divided into direct control users (direct control users), autonomous control users (automatic control users) and load aggregators according to the control mode of controllable loads. The direct control users are directly controlled by a power grid dispatching center, the load can be quickly cut off, the direct control users are large-capacity industrial users with energy storage or power generators, the demand response speed is high, the response quantity deviation is small, and the uncertainty is low; the self-control user autonomously removes the load after receiving the peak clipping response index, the load is distributed in industries such as industry, commerce and agriculture, the load types are various, so parameters such as response speed, duration and the like are related to the characteristics of various industries, the user considers response income and satisfaction degree, an optimal response strategy is adopted, and large deviation possibly exists between actual response capacity and the index; the load aggregator manages a large number of users, and after receiving indexes of a power grid dispatching center layer, the demand side resources are dispatched in modes of load rotation control and the like, so that the condition that the deviation of the response quantity is small can be guaranteed, and the response speed is influenced by the industry characteristics of the aggregation users.
Step 102, determining a load response requirement by a power grid dispatching center, wherein the load response requirement comprises a total demand index, a load range, response time, response duration and response speed, screening out users with the load range and the response speed up to the standard, calculating the response capability and response willingness of various users, distributing indexes for self-control, direct control and load aggregators, and aiming at minimizing the difference between the total user response quantity and the total demand index; the self-control users receive the indexes and then autonomously respond with an optimal response strategy, the load aggregator monitors and dispatches the resources on the demand side in a round-robin mode, the deviation of the response is ensured to be small, the direct-control users are directly controlled by a power grid dispatching center, and the loads are cut off without deviation.
Preferably, the specific method for constructing the typical industry load optimal response model in the rural power grid considering the user satisfaction in the step 2 is as follows:
step 201, considering user satisfaction, wherein the main body of a typical industry load optimal response model in the rural power grid is three types of users, namely, an automatic control user, a direct control user and a load aggregator user, the target function is the user with the maximum utility, and the user satisfaction and the response economic benefit need to be considered at the same time; MaxUi＝Ei+Ci
Figure BDA0003047456060000031
Figure BDA0003047456060000032
In the formula: u shapeiRepresenting the response utility of user i, by responding to economic profit EiAnd degree of user satisfaction CiTwo parts are formed; responsive economic benefit EiThe method comprises three parts of demand response excitation, unfinished index punishment and electric charge saving, wherein the demand response excitation subsidizes unit price rho for demand responseDRThe product of the response electric quantity, the excitation load (P)i,t-P0,i,t) Index response Ptar,iAnd upper limit ofNumber deltauThe product of the two; incomplete index penalty is the penalty price rhoFMultiplying the electric quantity of the part which does not reach the standard by an index response quantity Ptar,iCoefficient of lower limit deltadThe product of the two is used as a penalty criterion; the electricity price rho at the original time after the load response is considered in the process of saving the electricity feeE,tChange of the electric charge; degree of user satisfaction CiAccording to the difference between the user industry and the load type, the temperature control load expresses the user satisfaction C by the temperature difference of the demand response time intervalac(ii) a The user satisfaction of producing controllable load is divided into two parts of load change cost and load reduction loss, namely the kilowatt-hour value rho in the production process of each industrypdcProduct of the load total reduction on the day; the load change cost is the labor and management cost rho generated by unit load transferdpdcThe product of the current daily total fluctuation amount of the load; the satisfaction of electricity consumption for producing controllable load can be expressed as
Figure BDA0003047456060000033
In the power load of the third industry in the rural power grid, most of the adjustable load is air-conditioning load, and the power satisfaction degree of the user to the air-conditioning load is a piecewise function of the indoor temperature, namely:
Figure BDA0003047456060000041
Figure BDA0003047456060000042
Figure BDA0003047456060000043
in the formula: ctThe temperature control load satisfaction degree in the time period t;
Figure BDA0003047456060000044
is the indoor average temperature over time t; t iscomThe optimum temperature is set; sigma is ShuA moderate offset coefficient; delta T is the maximum tolerable temperature difference; cacAverage comfort of the temperature control load in the continuous time period of each demand response;
step 202, establishing an equivalent thermal parameter model representing the physical relation between the indoor temperature and the power of the air conditioning unit:
Figure BDA0003047456060000045
in the formula:
Figure BDA0003047456060000046
are each tc、tc-room temperature at time 1;
Figure BDA0003047456060000047
is tcThe ambient outdoor temperature at that time; r is equivalent thermal resistance; c is equivalent heat capacity; Δ tcIs the air conditioner control period;
and 203, performing conditional constraint on the model established in the step 202.
Preferably, the condition for performing the condition constraint in step 203 specifically includes a demand response period constraint, a response load constraint, a production controllable load constraint and a temperature controlled load constraint. The specific conditions are as follows:
demand response period constraint: the index response period is TDR,tarResponse start time t from indexst,tarAnd duration of response tdly,tarDetermine, and the actual response period T of user iDR,iThe response start time t decided by the userst,iAnd duration of response tdly,iThe influence where the response start time is the same as the start time given by the indicator, and the response duration is longer than the given duration of the indicator while being limited by the longest and shortest response durations in the user response capabilities.
TDR,tar＝tst,tar,tst,tar+1,...,tst,tar+tdly,tar
TDR,i＝tst,i,tst,i+1,...,tst,i+tdly,i
tst,i＝tst,tar
tdly,i≥tdly,tar
Figure BDA0003047456060000051
Responding to load constraint: the actual load is less than the baseline load during the demand response period; the load reduction is limited by the maximum and minimum response capability of the user; and the load response quantity of the load aggregation quotient and the direct control user is equal to the index response quantity.
0≤Pi,t≤P0,i,t(t∈TDR,tar)
Figure BDA0003047456060000052
Figure BDA0003047456060000053
P0,i,t-Pi,t＝Ptar,i,t(LA,DLC)
Production controllable load restraint: the production controllable load response quantity which can not be flexibly adjusted is integral multiple of the single machine response quantity.
Figure BDA0003047456060000054
Temperature control load restraint: the actual load of the temperature control load unit is always smaller than the maximum power of the unit
Figure BDA0003047456060000055
Preferably, the step 3 is to construct a hierarchical distributed diversity load cooperative scheduling optimization model, and the specific method for optimizing and solving the load scheduling policy in the emergency scene is as follows:
the main body of the hierarchical distributed type diversity load cooperative scheduling optimization model is a power grid scheduling center, the decision variable is a load response index distributed to each user, and the target function is the minimum deviation between the actual response quantity of the response duration period and the total index
Figure BDA0003047456060000056
In the formula: pi,tThe actual load of the user i after the response at the moment t; pi,t,0A load baseline of the user i at the time t; ptarIs a response total indicator; n is the total number of users participating in the response; t is tstIs the response start time; t is tdlyThe duration of the response.
Preferably, the constraint conditions of the hierarchical distributed diversity load collaborative scheduling optimization model include:
safety constraint: the sum of the response quantity of the response participating users is not less than the total index
Figure BDA0003047456060000061
And (3) response speed constraint: the response speed of the participating response user is not less than the index speed
vi≥vtar(i＝1,2,...,N)
In the formula: v. ofiResponse speed for user i; v. oftarIs an index response speed;
and (3) duration constraint: maximum response duration of the participating responding user is not less than index duration
Figure BDA0003047456060000062
In the formula:
Figure BDA0003047456060000063
maximum response duration for user i;
maximum response constraint: the response index should be less than the maximum response capability of the participating responding user
Figure BDA0003047456060000064
In the formula: ptar,iA load response index assigned to the user i;
Figure BDA0003047456060000065
the maximum response capacity of the user i at the time of the demand response t is obtained;
minimum response constraint: the response index should be greater than the minimum response limit of the participating responding users
Figure BDA0003047456060000066
In the formula:
Figure BDA0003047456060000067
the lowest response limit of the user i at the moment of the demand response t is set;
user intention constraint: user willingness to participate in demand response when user utility is greater than zero
Ui≥0(i＝1,2,...,N)
In the formula: u shapeiRepresenting the response utility of user i, the variable is passed by the lower layer.
Preferably, the method also comprises a step 4 of solving a layered distributed diversity load cooperative scheduling optimization model to obtain the optimal scheduling index P of each usertar,iAnd response start time, response duration, response load index and response speed, namely the load scheduling strategy in the emergency scene.
The substantial effects of the invention are as follows: 1) according to the typical industry load optimal response model in the rural power grid considering the user satisfaction, the power utilization satisfaction and the response economic benefit are comprehensively considered, a user optimal demand response multi-objective optimization model is built, and user response willingness and load distribution strategies under different demand response indexes and time periods are analyzed. A foundation is laid for researching a load scheduling strategy in an emergency scene;
2) the method models a load scheduling strategy in an emergency scene, and by means of a hierarchical distributed load cooperative scheduling system facing a rural power grid, the completion rate of demand response indexes in the rural power grid is improved, so that the operation flexibility and safety of the rural power grid are improved;
3) the method is easy to operate, provides an optimal index distribution strategy for demand response in a rural power grid emergency scene, and has certain practical significance.
Drawings
Fig. 1 is a schematic overall flow chart of the first embodiment.
Fig. 2 is a structural diagram of a hierarchical distributed diversity load cooperative scheduling system according to an embodiment.
FIG. 3 is a time-lapse electricity rate graph of the embodiment.
Fig. 4 is a load curve diagram after the hierarchical distributed load cooperative scheduling for the rural power grid in the second embodiment.
Fig. 5 is a load curve diagram after the hierarchical distributed load cooperative scheduling for the rural power grid is adopted on the premise that the user response behavior is not considered in the second embodiment.
Detailed Description
The following provides a more detailed description of the present invention, with reference to the accompanying drawings.
Fig. 1 is a schematic overall flow chart of the present invention. The invention relates to a hierarchical distributed load cooperative scheduling method for a rural power grid, which comprises the following steps:
step 1: a hierarchical distributed load cooperative scheduling system facing a rural power grid is built, and a diversity load scheduling flow under an emergency scene is designed. The specific implementation method of the step is as follows:
the layered distributed diversified load cooperative scheduling system comprises a load scheduling center layer, a load aggregation business layer and a demand side resource layer. The demand side resource layer comprises direct control users, autonomous control users and small-scale users of load aggregation agent agents.
In the hierarchical distributed diversified load cooperative scheduling system, users are divided into direct control users (direct control users), autonomous control users (automatic control users) and load aggregators according to the control mode of controllable loads. The direct control users are directly controlled by a power grid dispatching center, the load can be quickly cut off, the direct control users are large-capacity industrial users with energy storage or power generators, the demand response speed is high, the response quantity deviation is small, and the uncertainty is low; the self-control user autonomously removes the load after receiving the peak clipping response index, the load is distributed in industries such as industry, commerce and agriculture, the load types are various, so parameters such as response speed, duration and the like are related to the characteristics of various industries, the user considers response income and satisfaction degree, an optimal response strategy is adopted, and large deviation possibly exists between actual response capacity and the index; the load aggregator manages a large number of users, and after receiving indexes of a power grid dispatching center layer, the demand side resources are dispatched in modes of load rotation control and the like, so that the condition that the deviation of the response quantity is small can be guaranteed, and the response speed is influenced by the industry characteristics of the aggregation users.
When the diversified load scheduling is carried out in an emergency scene, firstly, a power grid scheduling center determines load response requirements including a total demand index, a load range, response time, response duration and response speed, screens out users with the load range and the response speed up to the standard, calculates the response capacity and response willingness of various users, distributes indexes for automatic control, direct control and load aggregation quotient, and aims to minimize the difference between the total user response quantity and the total demand index; the self-control users receive the indexes and then autonomously respond with an optimal response strategy, the load aggregator monitors and dispatches the resources on the demand side in a round-robin mode, the deviation of the response is ensured to be small, the direct-control users are directly controlled by a power grid dispatching center, and the loads are cut off without deviation.
Step 2: and constructing a typical industry load optimal response model in the rural power grid considering the user satisfaction. The specific implementation method of the step is as follows:
the typical industry load optimal response model in the rural power grid considering the user satisfaction degree mainly comprises three types of users, namely automatic control users, direct control users and load aggregators, the target function is the user with the maximum effectiveness, and the user satisfaction degree and the response economic benefit need to be considered at the same time.
maxUi＝Ei+Ci
Figure BDA0003047456060000081
Figure BDA0003047456060000082
In the formula: u shapeiRepresenting the response utility of user i, by responding to economic profit EiAnd degree of user satisfaction CiTwo parts are formed. Responsive economic benefit EiThe method comprises three parts of demand response excitation, unfinished index punishment and electric charge saving, wherein the demand response excitation subsidizes unit price rho for demand responseDRThe product of the response electric quantity, the excitation load (P)i,t-P0,i,t) Index response Ptar,iWith an upper limit factor deltauThe product of the two; incomplete index penalty is the penalty price rhoFMultiplying the electric quantity of the part which does not reach the standard by an index response quantity Ptar,iCoefficient of lower limit deltadThe product of the two is used as a penalty criterion; the electricity price rho at the original time after the load response is considered in the process of saving the electricity feeE,tThe electricity fee varies.
Degree of user satisfaction CiAccording to the difference between the user industry and the load type, the temperature control load expresses the user satisfaction C by the temperature difference of the demand response time intervalac(ii) a The user satisfaction of producing controllable load is divided into two parts of load change cost and load reduction loss, namely the kilowatt-hour value rho in the production process of each industrypdcProduct of the load total reduction on the day; the load change cost is the labor and management cost rho generated by unit load transferdpdcThe product of the amount of total daily fluctuation of the load. The satisfaction of electricity consumption for producing controllable load can be expressed as
Figure BDA0003047456060000083
In the power load of the third industry in the rural power grid, most of the adjustable load is air-conditioning load, and the power satisfaction degree of the user to the air-conditioning load is a piecewise function of the indoor temperature, namely:
Figure BDA0003047456060000091
Figure BDA0003047456060000092
Figure BDA0003047456060000093
in the formula: ctThe temperature control load satisfaction degree in the time period t;
Figure BDA0003047456060000094
is the indoor average temperature over time t; t iscomThe optimum temperature is set; σ is comfort shift coefficient; delta T is the maximum tolerable temperature difference; cacThe average comfort level of the temperature controlled load over the duration of each demand response.
The equivalent thermal parameter model representing the physical relationship between the indoor temperature and the power of the air conditioning unit is as follows:
Figure BDA0003047456060000095
in the formula:
Figure BDA0003047456060000096
are each tc、tc-room temperature at time 1;
Figure BDA0003047456060000097
is tcThe ambient outdoor temperature at that time; r is equivalent thermal resistance; c is equivalent heat capacity; Δ tcIs an air conditioning control period.
The constraints of the model include:
demand response period constraint: the index response period is TDR,tarResponse start time t from indexst,tarAnd duration of response tdly,tarDetermine, and the actual response period T of user iDR,iThe response start time t decided by the userst,iAnd duration of response tdly,iThe influence where the response start time is the same as the start time given by the indicator, and the response duration is longer than the given duration of the indicator while being limited by the longest and shortest response durations in the user response capabilities.
TDR,tar＝tst,tar,tst,tar+1,...,tst,tar+tdly,tar
TDR,i＝tst,i,tst,i+1,...,tst,i+tdly,i
tst,i＝tst,tar
tdly,i≥tdly,tar
Figure BDA0003047456060000101
Responding to load constraint: the actual load is less than the baseline load during the demand response period; the load reduction is limited by the maximum and minimum response capability of the user; and the load response quantity of the load aggregation quotient and the direct control user is equal to the index response quantity.
0≤Pi,t≤P0,i,t(t∈TDR,tar)
Figure BDA0003047456060000102
Figure BDA0003047456060000103
P0,i,t-Pi,t＝Ptar,i,t(LA,DLC)
Production controllable load restraint: the production controllable load response quantity which can not be flexibly adjusted is integral multiple of the single machine response quantity.
Figure BDA0003047456060000104
Temperature control load restraint: temperature control load machineThe actual load of the group should always be less than the maximum power of the unit
Figure BDA0003047456060000105
And step 3: and constructing a layered distributed diversity load cooperative scheduling optimization model, and optimizing and solving a load scheduling strategy in an emergency scene. The specific implementation method of the step is as follows:
the main body of the hierarchical distributed type diversity load cooperative scheduling optimization model is a power grid scheduling center, the decision variable is a load response index distributed to each user, and the target function is the minimum deviation between the actual response quantity of the response duration period and the total index
Figure BDA0003047456060000106
In the formula: pi,tThe actual load of the user i after the response at the moment t; pi,t,0A load baseline of the user i at the time t; ptarIs a response total indicator; n is the total number of users participating in the response; t is tstIs the response start time; t is tdlyThe duration of the response.
The constraints of the model include:
safety constraint: the sum of the response quantity of the response participating users is not less than the total index
Figure BDA0003047456060000107
And (3) response speed constraint: the response speed of the participating response user is not less than the index speed
vi≥vtar(i＝1,2,...,N)
In the formula: v. ofiResponse speed for user i; v. oftarIs an index of response speed.
And (3) duration constraint: maximum response duration of the participating responding user is not less than index duration
Figure BDA0003047456060000108
In the formula:
Figure BDA0003047456060000111
maximum response duration for user i;
maximum response constraint: the response index should be less than the maximum response capability of the participating responding user
Figure BDA0003047456060000112
In the formula: ptar,iA load response index assigned to the user i;
Figure BDA0003047456060000113
the maximum response capacity of the user i at the time of the demand response t is obtained;
minimum response constraint: the response index should be greater than the minimum response limit of the participating responding users
Figure BDA0003047456060000114
In the formula:
Figure BDA0003047456060000115
the lowest response limit for user i at the time of demand response t.
User intention constraint: and when the user utility is more than zero, the user has the intention to participate in demand response.
Ui≥0(i＝1,2,...,N)
In the formula: u shapeiRepresenting the response utility of user i, the variable is passed by the lower layer.
The optimal scheduling index P of each user can be obtained by solving the hierarchical distributed diversity load cooperative scheduling optimization modeltar,iAnd response starting time, response duration, response load index and response speed, namely a load scheduling strategy in an emergency scene.
Example two:
the embodiment adopts the load data of industrial, commercial and agricultural users in a certain area for model verification, and the time-of-use electricity price curve of the area is shown as the attached figure 2. Taking a typical day of summer as an example, the demand response start time and duration are set to 14:00 and 2 hours, respectively. The demand response sets an incentive price of 4.0 RMB/kWh, an incentive upper limit of 120%, a penalty price of 5.0 rmb/kWh, and an incentive lower limit of 80%. Three typical cases are set up: 1)5MW load shortage and low response speed requirement; 2) the 10MW load is in shortage, and the response speed requirement is low; 3) the 10MW load is short, and the response speed requirement is high. Table 1 shows the optimal load co-scheduling strategy in three typical cases. Table 2 shows the completion of the demand response indicator for three typical cases.
TABLE 1 optimal load Co-scheduling strategy
Figure BDA0003047456060000116
TABLE 2 demand response indicator completion
Figure BDA0003047456060000117
Figure BDA0003047456060000121
The deviation between the response quantity and the load index can be controlled within 1.0%, the deviation between the response quantity and the load index is increased along with the increase of the load index, and the deviation is increased along with the increase of the required response speed under the same load index.
Taking the calculation example 1 in the table 2 as an example, the peak clipping index is 5000kW, the response time period is 14:00-16:00, the response notification time is 12:00, the attached figure 4 is a load curve after the hierarchical distributed load collaborative scheduling facing the rural power grid, and 0:00-12:00 in the graph represents a next-day power utilization curve. Within 2 hours of continuous response, the load reduction is 5015kW and 5012kW respectively, and the difference between the actual response and the index is within 0.03%; the load is slightly increased one hour before the response starts and one hour after the response ends, the load is increased to 54.95kW and 257.3kW, and the load is mainly influenced by the air conditioning load of commercial users; after the response is finished, the load rises again in the load valley period of 0:00-7:00 the next day, which is mainly influenced by three-shift industrial users, and the users transfer the response load to the period with lower electricity price, and the maximum load rises again by 2584 kW. The load response situation is shown in fig. 5 when the response indexes are distributed averagely only according to the response capacity reported by the user, regardless of the user response behavior. Within 2 hours of continuous response, the load reduction is 5662kW and 4569kW respectively, the difference between the actual corresponding quantity and the index is 13.24%, -8.62%, and the difference between the response quantity and the index of the example 1 is within 0.03%, which shows that the demand response index distribution strategy considering the user intention and the response behavior can improve the completion rate of the demand response index in the rural power grid, and is beneficial to improving the operation flexibility and the safety of the rural power grid.
The above embodiment is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and other variations and modifications may be made without departing from the technical scope of the claims.

Claims (7)

1. A hierarchical distributed load cooperative scheduling method facing a rural power grid is characterized by comprising the following steps:
step 1: constructing a hierarchical distributed load cooperative scheduling system facing a rural power grid, and designing a diversity load scheduling flow under an emergency scene;
step 2: constructing a typical industry load optimal response model in a rural power grid considering user satisfaction;
and step 3: and constructing a layered distributed diversity load cooperative scheduling optimization model, and optimizing and solving a load scheduling strategy in an emergency scene.
2. The rural power grid-oriented hierarchical distributed load cooperative scheduling method according to claim 1, wherein: the method comprises the following steps that a hierarchical distributed load cooperative scheduling system facing the rural power grid is built in the step 1, and a specific method for designing a diversity load scheduling process in an emergency scene is as follows:
101, a hierarchical distributed diversified load cooperative scheduling system comprises a load scheduling center layer, a load aggregation business layer and a demand side resource layer; wherein the resource layer on demand side comprises direct control users, autonomous control users and small-scale users of load aggregation agent
Step 102, determining a load response requirement by a power grid dispatching center, wherein the load response requirement comprises a total demand index, a load range, response time, response duration and response speed, screening out users with the load range and the response speed up to the standard, calculating the response capability and response willingness of various users, distributing indexes for self-control, direct control and load aggregators, and aiming at minimizing the difference between the total user response quantity and the total demand index; the self-control users receive the indexes and then autonomously respond with an optimal response strategy, the load aggregator monitors and dispatches the resources on the demand side in a round-robin mode, the deviation of the response is ensured to be small, the direct-control users are directly controlled by a power grid dispatching center, and the loads are cut off without deviation.
3. The rural power grid-oriented hierarchical distributed load cooperative scheduling method according to claim 1, wherein: the specific method for constructing the typical industry load optimal response model in the rural power grid considering the user satisfaction in the step 2 is as follows:
step 201, considering user satisfaction, wherein the main body of a typical industry load optimal response model in the rural power grid is three types of users, namely, an automatic control user, a direct control user and a load aggregator user, the target function is the user with the maximum utility, and the user satisfaction and the response economic benefit need to be considered at the same time;
max Ui＝Ei+Ci
Figure FDA0003047456050000021
Figure FDA0003047456050000022
in the formula: u shapeiRepresenting the response utility of user i, by responding to economic profit EiAnd degree of user satisfaction CiTwo parts are formed; responsive economic benefit EiThe method comprises three parts of demand response excitation, unfinished index punishment and electric charge saving, wherein the demand response excitation subsidizes unit price rho for demand responseDRThe product of the response electric quantity, the excitation load (P)i,t-P0,i,t) Index response Ptar,iWith an upper limit factor deltauThe product of the two; incomplete index penalty is the penalty price rhoFMultiplying the electric quantity of the part which does not reach the standard by an index response quantity Ptar,iCoefficient of lower limit deltadThe product of the two is used as a penalty criterion; the electricity price rho at the original time after the load response is considered in the process of saving the electricity feeE,tChange of the electric charge; degree of user satisfaction CiAccording to the difference between the user industry and the load type, the temperature control load expresses the user satisfaction C by the temperature difference of the demand response time intervalac(ii) a The user satisfaction of producing controllable load is divided into two parts of load change cost and load reduction loss, namely the kilowatt-hour value rho in the production process of each industrypdcProduct of the load total reduction on the day; the load change cost is the labor and management cost rho generated by unit load transferdpdcThe product of the current daily total fluctuation amount of the load; the satisfaction of electricity consumption for producing controllable load can be expressed as
Figure FDA0003047456050000023
In the power load of the third industry in the rural power grid, most of the adjustable load is air-conditioning load, and the power satisfaction degree of the user to the air-conditioning load is a piecewise function of the indoor temperature, namely:
Figure FDA0003047456050000024
Figure FDA0003047456050000031
Figure FDA0003047456050000032
in the formula: ctThe temperature control load satisfaction degree in the time period t;
Figure FDA0003047456050000033
is the indoor average temperature over time t; t iscomThe optimum temperature is set; σ is comfort shift coefficient; delta T is the maximum tolerable temperature difference; cacAverage comfort of the temperature control load in the continuous time period of each demand response;
step 202, establishing an equivalent thermal parameter model representing the physical relation between the indoor temperature and the power of the air conditioning unit:
Figure FDA0003047456050000034
in the formula:
Figure FDA0003047456050000035
are each tc、tc-room temperature at time 1;
Figure FDA0003047456050000036
is tcThe ambient outdoor temperature at that time; r is equivalent thermal resistance; c is equivalent heat capacity; Δ tcIs the air conditioner control period;
and 203, performing conditional constraint on the model established in the step 202.
4. The rural power grid-oriented hierarchical distributed load cooperative scheduling method according to claim 3, wherein: the condition for performing the condition constraint in step 203 specifically includes a demand response time period constraint, a response load constraint, a production controllable load constraint, and a temperature control load constraint.
5. The rural power grid-oriented hierarchical distributed load cooperative scheduling method according to claim 1, wherein: the specific method for constructing the hierarchical distributed diversity load cooperative scheduling optimization model in the step 3 and optimizing and solving the load scheduling strategy in the emergency scene is as follows:
the main body of the hierarchical distributed type diversity load cooperative scheduling optimization model is a power grid scheduling center, the decision variable is a load response index distributed to each user, and the target function is the minimum deviation between the actual response quantity of the response duration period and the total index
Figure FDA0003047456050000041
In the formula: pi,tThe actual load of the user i after the response at the moment t; pi,t,0A load baseline of the user i at the time t; ptarIs a response total indicator; n is the total number of users participating in the response; t is tstIs the response start time; t is tdlyThe duration of the response.
6. The rural power grid-oriented hierarchical distributed load cooperative scheduling method according to claim 5, wherein the constraint conditions of the hierarchical distributed diversity load cooperative scheduling optimization model include:
safety constraint: the sum of the response quantity of the response participating users is not less than the total index
Figure FDA0003047456050000042
And (3) response speed constraint: the response speed of the participating response user is not less than the index speed
vi≥vtar (i＝1,2,...,N)
In the formula: v. ofiResponse speed for user i; v. oftarIs an index response speed;
and (3) duration constraint: maximum response duration of the participating responding user is not less than index duration
Figure FDA0003047456050000043
In the formula:
Figure FDA0003047456050000044
maximum response duration for user i;
maximum response constraint: the response index should be less than the maximum response capability of the participating responding user
Figure FDA0003047456050000045
In the formula: ptar,iA load response index assigned to the user i;
Figure FDA0003047456050000046
the maximum response capacity of the user i at the time of the demand response t is obtained;
minimum response constraint: the response index should be greater than the minimum response limit of the participating responding users
Figure FDA0003047456050000047
In the formula:
Figure FDA0003047456050000048
the lowest response limit of the user i at the moment of the demand response t is set;
user intention constraint: user willingness to participate in demand response when user utility is greater than zero
Ui≥0 (i＝1,2,...,N)
In the formula: u shapeiRepresenting the response utility of user i, the variable is passed by the lower layer.
7. The rural power grid-oriented hierarchical distributed load cooperative scheduling method according to claim 1, wherein: step 4, solving the hierarchical distributed diversity load cooperative scheduling optimization model to obtain the optimal scheduling index P of each usertar,iAnd response start time, response duration, response load index and response speed, namely the load scheduling strategy in the emergency scene.
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